Recent advances in Artificial Intelligence and Machine Learning are enabling a new generation of underwater robots to make intelligent decisions on navigation, manipulation, and sampling by reasoning about their environment in the context of prior data. At USC, our lab is engaged in a long-term effort to develop persistent, autonomous underwater robotic systems. In this talk, I will describe some of our recent results focusing on two problems in adaptive sensing (underwater change detection) and sample collection (biological sampling). I will also give a brief overview of our work on hazard avoidance, allowing robots to operate in regions where there is substantial ship traffic, and some results on coordination in multirobot deployments in the ocean.